Abstract

Separable spatio-temporal covariance models, defined as the product of purely spatial and purely temporal covariance functions, are often used in practice, but frequently they only represent a convenient assumption. On the other hand, non-separable models are receiving a lot of attention, since they are more flexible to handle empirical covariances showed up in applications. Different forms of non-separability for space–timecovariance functions have been recently defined in the literature. In this paper, the notion of positive and negativenon-separability is further formalized in order to distinguish between pointwise and uniform non-separability. Various well-known non-separable space–time stationary covariance models are analyzed and classified by using the new definition of non-separability. In particular, wide classes of non-separable spatio-temporal covariance functions, able to capture positive and negativenon-separability, are proposed and some examples of these classes are given. General results concerning the non-separability of spatial–temporal covariance functions obtained by a linear combination of spatial–temporal covariance functions and some stability properties are also presented. These results can be helpful to generate as well as to select appropriate covariance models for describing space–time data.

Autore Pugliese

• De Iaco Sandra , Posa Donato

Tutti gli autori

• De Iaco S. , Posa D.

Titolo volume/Rivista

JOURNAL OF STATISTICAL PLANNING AND INFERENCE

Anno di pubblicazione

2013

ISSN

0378-3758

ISBN

Non Disponibile

Numero di citazioni Wos

11

Ultimo Aggiornamento Citazioni

28/04/2018

Numero di citazioni Scopus

12

Ultimo Aggiornamento Citazioni

28/04/2018

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile